Results from search providers using a browsing-time relevancy factor

ABSTRACT

A method for searching Web pages that begins with the identification of query criteria entered into a search provider. A set of Web pages that satisfies the query criteria are determined. Then, a page ranking is ascertained for each Web page in the set. The Web pages are presented in order by page ranking. The page ranking is based upon at least one relevancy factor that includes a browsing-time factor. The browsing-time factor can be calculated from browsing behavior exhibited by users, who provided similar query criteria. The set of users from which the browsing-time factor is calculated can include a current user, a set of users sharing characteristics with the current user, and/or a general set of users. Browsing behavior can include time spent at a Web page, where the browsed Web page is a page that was previously presented as a search result for the similar query criteria.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application of and which claims the benefit ofU.S. patent application Ser. No. 11/460,038, filed 26 Jul. 2006 andwhich is hereby incorporated by reference.

BACKGROUND Field of the Invention

The present invention relates to the field of Web page searches and,more particularly, improving search results using a browsing-timerelevancy factor.

Description of the Related Art

Large knowledge stores, such as a corporate intranet or the Internet,contain such a vast amount of information that finding accurateinformation in a timely manner is a daunting task. A variety of searchengines exist that promise to provide users with fast and, moreimportantly, accurate results for their queries. However, the methodsused by conventional search engines to determine the ordering of searchresults in terms of relevance to a user's query can be externallymanipulated. This manipulation decreases the capability of conventionalsearch engines to provide accurate results, increases the time spent byusers to identify relative information, and increases a user's sense offrustration.

For example, the presence of the search terms or query criteria in thetext of a Web page typically implies that the Web page is relevant tothe user's query. Therefore, the more times the query criteria appearson a Web page, the more relevance the page is to the query and thehigher it should appear in the results list. This is a part of thesimple relevancy logic of many early search engines. Once Web pageauthors realized that key word repetition in page content increased apage's ranking, the validity of this relevancy factor diminished. Byrepeating words “invisibly”, white text on a white background, a Webpage author could increase a page's ranking without providing additionalrelevance or information. Similarly, unrelated words can be added to aWeb page so that the page is included in the results of more popularsearch terms.

Conventional search engines have attempted to overcome the flagrantabuses of their ranking algorithms. Many popular search engines use avariety of factors and weightings to determine the relevancy of a Webpage to the entered query criteria. Despite these improvements, theresults provided are still skewed. For example, many search engines sellorganizations the ability to increase the ranking of Web pages from adesignated Web site.

Further, many of the factors used by conventional search engines tocombat abuse inject additional biases. For example, the age of a Webpage or Web site is often used to determine a sense of legitimacy. Thisfactor, meant to contend with influxes of fad and fictitious Web sites,precludes relevant content simply because its host is newer than theexisting majority. Another factor uses a frequency with which a Web pageis accessed, under the assumption that popular Web pages are morerelevant to users than less popular ones. This popularity factor isabused by automated systems that iteratively access a Web page, with theexplicit purpose of artificially inflating a popularity factorassociated with the page.

SUMMARY OF THE INVENTION

The present invention discloses a solution for improving results from asearch provider using a browsing-time relevancy factor. Thebrowsing-time factor represents a time that a user spends viewing a Webpage, which was Web page returned by a search provider responsive touser provided query criteria. It is assumed that a user will spend anappreciable time browsing a Web page that is relevant for a user's queryand less time viewing an irrelevant Web page. The present inventionovercomes the manipulation of search results by external and irrelevantfactors by including the influence of actual result usage.

The present invention can be implemented in accordance with numerousaspects consistent with material presented herein. For example, oneaspect of the present invention can include a method for searching Webpages that begins with the identification of query criteria entered intoa search provider. A set of Web pages that satisfy the query criteriacan be determined. Then, a page ranking can be ascertained for each Webpage in the set. The Web pages can be presented in order by pageranking. The page ranking can be based upon at least one relevancyfactor that includes a browsing-time factor. The browsing-time factorcan be based upon a time spent at a browsed Web page.

Another aspect of the present invention can include a method forordering a set of Web pages that satisfy query criteria. Each Web pagein a set of Web pages that satisfy the query criteria can be assigned apage ranking value. The Web pages can then be ordered in the set inaccordance with the assigned page ranking values. Subsequently, the Webpages can be presented in order within the search Web page where thequery criteria were entered. The page ranking value can be based atleast in part upon a browsing-time factor. The browsing-time factor canbe based at least in part upon a time spent by at least one user at anassociated browsed Web page, during a previous query having criteriasimilar to the query criteria.

Yet another aspect of the present invention can include a system forsearching Web pages. This system can include a search provider and aresult refinement engine. The search provider can be configured tosearch Web pages that satisfy user-entered query criteria. The resultrefinement engine can be configured to apply relevancy algorithms thatinclude a browsing-time factor.

It should be noted that various aspects of the invention can beimplemented as a program for controlling computing equipment toimplement the functions described herein, or a program for enablingcomputing equipment to perform processes corresponding to the stepsdisclosed herein. This program may be provided by storing the program ina magnetic disk, an optical disk, a semiconductor memory, any otherrecording medium, or can also be provided as a digitally encoded signalconveyed via a carrier wave. The described program can be a singleprogram or can be implemented as multiple subprograms, each of whichinteract within a single computing device or interact in a distributedfashion across a network space.

The method detailed herein can also be a method performed at least inpart by a service agent and/or a machine manipulated by a service agentin response to a service request.

BRIEF DESCRIPTION OF THE DRAWINGS

There are shown in the drawings, embodiments which are presentlypreferred, it being understood, however, that the invention is notlimited to the precise arrangements and instrumentalities shown.

FIG. 1 is a schematic diagram illustrating a system for searching Webpages utilizing a browsing-time factor in accordance with embodiments ofthe inventive arrangements disclosed herein.

FIG. 2 is an illustration of the components of a result refinementengine in accordance with an embodiment of the inventive arrangementsdisclosed herein.

FIG. 3 is a collection of illustrations depicting the influence of abrowsing-time factor on Web page search results in accordance with anembodiment of the inventive arrangements disclosed herein.

FIG. 4 is a flow chart of a method for searching Web pages utilizing abrowsing-time factor in accordance with an embodiment of the inventivearrangements disclosed herein.

FIG. 5 is a flow chart of a method where a service agent can configure asystem for searching Web pages utilizing a browsing-time factor inaccordance with an embodiment of the inventive arrangements disclosedherein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic diagram illustrating a system 100 for searchingWeb pages utilizing a browsing-time factor in accordance withembodiments of the inventive arrangements disclosed herein. System 100can include client 110, workgroup/domain 112, and server 120communicatively linked through network 115.

Client 110 is used by user 105 to access search provider 125 of server120 in order to search Web pages contained within network 115. Client110 can be any of a variety of computing devices including, but notlimited to, a personal computer, a kiosk, a personal data assistant(PDA), a mobile phone, and the like. Contained within or attached toclient 110 are behavior capture engine 113 and behavior data store 116.

Behavior capture engine 113 can include a set of machine-readableinstructions capable of capturing data related to actions performed byuser 105 on client 110. Behavior capture engine 113 can be any of avariety of software application types including, but not limited to, athin client, a client application, a local stand-alone application, aWeb-based application, an applet, and the like. The data captured bybehavior capture engine 113 is stored within behavior data store 116.

In another contemplated embodiment, behavior capture engine 113 andbehavior data store 116 may be omitted from client 110 in favor of theaddition of behavior capture engine 114 and behavior data store 118 toworkgroup/domain 112. This configuration allows for system flexibilityand an efficient use of system resources. For instance, client 110 maynot have the proper system resources to accommodate behavior captureengine 113 and behavior data store 116. Additionally, this configurationallows for a centralized location of data and software. Further, thisconfiguration permits Web behavior to be captured for a group of users,in an anonymous and/or transparent fashion.

Server 120 can be any type of network server that can support searchprovider 125 and return-to-engine timer 122. For example, a server 120can includes a Web server that utilizes one or more search engines topresent Web pages to users believed to be relevant for user specifiedquery criteria. Server 120 can include a Web server associated with asingle search engine, such as a Web server for GOOGLE.COM or YAHOO.COM,as well as a Web server associated with a metasearch engine, such as aWeb server for DOGPILE.COM or MAMMA.COM, that utilizes multiple searchengines.

Return-to-engine timer 122 can include a set of machine-readableinstructions capable of measuring the elapsed time before user 105returns to search provider 125 for the purposes of determining abrowsing-time factor. Return-to-engine time 122 can be any of a varietyof software application types including, but not limited to, a serverapplication, a local stand-alone application, a Web-based application,an applet, and the like.

Search provider 125 can be any type of software application or searchengine capable of searching Web pages and utilizing result refinementengine 135 and search interaction data store 130. Search provider 125can be implemented in a variety of manners including, but not limitedto, a conventional search engine, a search script, a meta-search engine,and the like. Search provider 125 can generate responses to querycriteria using crawler-based search engines including, but not limitedto, ALLTHEWEB, ALTAVISTA, GOOGLE, INKTOMI, TEOMA, and combinations orderivatives thereof.

Result refinement engine 135 can include a set of machine-readableinstructions capable of determining Web page relevancy based in partupon a browsing-time factor. The browsing-time factor can be one of manyfactors used by a search engine to determine a relevancy of a set of Webpages for user provided query criteria. Result refinement engine 135 canbe implemented as any of a variety of software application typesincluding, but not limited to, a web service, a server application, alocal stand-alone application, a Web server application plug-incomponent, an applet, and the like. The data required by resultrefinement engine 135 is stored within search interaction data store130.

As used herein, presented data stores, including stores 116, 118, and130, can be a physical or virtual storage space configured to storedigital information. Data stores 116, 118, and 130 can be physicallyimplemented within any type of hardware including, but not limited to, amagnetic disk, an optical disk, a semiconductor memory, a digitallyencoded plastic memory, a holographic memory, or any other recordingmedium. Each of the data stores 116, 118, and 130 can be a stand-alonestorage unit as well as a storage unit formed from a plurality ofphysical devices. Additionally, information can be stored within datastore 116, 118, and/or 130 in a variety of manners. For example,information can be stored within a database structure or can be storedwithin one or more files of a file storage system, where each file mayor may not be indexed for information searching purposes. Further, datastores 116, 118, and/or 130 can utilize one or more encryptionmechanisms to protect stored information from unauthorized access.

Network 115 can include any hardware/software/and firmware necessary toconvey data encoded within carrier waves. Data can be contained withinanalog or digital signals and conveyed though data or voice channels.Network 115 can include local components and data pathways necessary forcommunications to be exchanged among computing device components andbetween integrated device components and peripheral devices. Network 115can also include network equipment, such as routers, data lines, hubs,and intermediary servers which together form a data network, such as theInternet. Network 115 can also include circuit-based communicationcomponents and mobile communication components, such as telephonyswitches, modems, cellular communication towers, and the like. Network115 can include line based and/or wireless communication pathways.

FIG. 2 is an illustration 200 of the components of result refinementengine 205 in accordance with an embodiment of the inventivearrangements disclosed herein. Illustration 200 can be performed in thecontext of system 100. The example of illustration 200 is not limited inthis regard, however, and can be performed in the context of any systemsupporting the use of a browsing-time factor for improving Web pagesearch results.

Result refinement engine 205 can contain legacy factors 220, factorweighting engine 230, and browsing-time factor components 210.Browsing-time factor components 210 can contain browsing factorcalculator 212, skewing protection 214, similarity function 216, andhistorical influencing 218.

Browsing factor calculator 212 can represent the algorithms andcorresponding values of significance associated with the calculation ofthe browsing-time factor for a browsed Web page. Browsing factorcalculation 212 can utilize the timing data gathered by return-to-enginetimer 122 and/or behavior capture engine 113-114 of system 100. Thebrowsing-time factors calculated by browsing factor calculator 212 canbe maintained within search interaction data store 130 of system 100.

The browsing-time factor computed by the browsing factor calculator 212is a factor used to generally denote browsing behavior and is not to beconstrued as limited to a blunt time spent browsing a Web page. Instead,the browsing-time factor can be include any condition relating tobrowsing that indicates a user interest in a browsed Web page, whetherthat interest is positive or negative. Additional considerations, suchas a time-out period of inactivity that indicates abandonment of acomputer, as opposed to interest in a Web page and a subsequent returnto a browsed Web page can be adjusted for by calculator 212.

For example, positive adjustment events for calculator 212 that indicateuser interest can include such events as scrolling to see a complete Webpage, viewing an entire Web page, downloading content from a browsed Webpage, printing a Web page, copying content from a Web page, making apurchase from a Web page, adding a Web page to a list of bookmarkedpages, and the like. Examples of negative adjustment events forcalculator 212 can include not viewing an entire Web page, blocking aWeb page using a firewall, and the like.

The calculator 212 can apply different point values to each of thepositive and negative factors and can calculate a browsing-time factorbased upon a total of these points. In one embodiment, points assignedbased upon time spent at a Web page can be assigned based on the belowtable, which assumes that a browsing-time factor is computed as afunction of a return-to-search-engine-time, which can be a computationbased upon data available to return-to-engine timer 122 of system 100.

Points Calculation Event −5 Less than five seconds and then viewed thenext result 0 Between five and ten seconds and then viewed the nextresult 5 Between ten and twenty seconds and then viewed the next result10 Between twenty and thirty seconds and then viewed the next result 15If a searcher doesn't return for another result that day 20 If thesearcher returned to a search provider less than an hour later with anew phrase with a similarly score of less than 0.75 25 If the searcherreturned to a search provider less than an hour later with a new phrasewith a similarly score of less than 0.50 30 If the searcher returned toa search provider less than an hour later with a new phrase with asimilarly score of less than 0.20 40 If the searcher returned to asearch provider less than an hour later with a new phrase with asimilarly score of less than 0.10 50 If the searcher returned to asearch provider less than an hour later with a new phrase with asimilarly score of less than 0.05

In a different embodiment, points for a browsing-time factor can bebased in part upon behavioral events, such as those shown in a tablebelow, which can be gathered by behavioral capture engine 113 and/or 114of system 100. The behavioral events can optionally be combined withbrowsing times (not shown).

Points Calculation Event −70 If first searcher firewalled page 10 Iffirst searcher viewed entire page (i.e. scrolled down) 20 If firstsearcher downloaded a file 25 If first searcher printed the page 30 Iffirst searcher made a purchase 50 if first searcher made a largepurchase 55 If first searcher bookmarked page

The tables, point values, and calculations events are not intended to beexhaustive, but are instead intended to illustrate a concept expressedherein. The various calculation events and point values can be userconfigurable and/or administrator configurable values. Further, anoptimization engine (not shown) can automatically adjust thepoint-values and/or events based upon analyzed historical data.

Skewing protection 214 can represent the algorithms and correspondingvalues of significance meant to counteract excessive externalinfluencing of search results. Web site or user identifications can bedesignated with corresponding adjustments in order to limit the effectof the designated Web site or user. For example, the influence of userswho test Web pages for quality assurance reasons can be reduced toreflect their visitation of Web pages for non-informational purposes.Skewing protection 214 can assure that no single computing device, user,and/or IP address can dominate a browsing-time factor. For example,skewing protection 214 can disregard input from a particular computingdevice, user, and/or IP address after a fixed number of accesses pertime period have occurred, which prevents or limits the effect ofpurposeful biasing of a browsing-time factor. The information necessaryfor the functioning of skewing protection 214 can be maintained withinsearch interaction data store 130 of system 100.

Similarity function 216 can represent the algorithms and correspondingvalues of significance to determine the similarity of the current querycriteria to the query criteria of previous searches performed by otherusers. For example, the similarity function 216 can ensure that a querycriteria of “cars” is similar to query criteria of “automobiles” and isthus subject to similar handling from a browsing-time factorperspective. This can be accomplished in a variety of manners, such asthe use of existing algorithms for the determination of stringsimilarity, such as Hamming distance based algorithm, Levenshteindistance based algorithm, a Huffman compression based similarlyalgorithm, or an internally developed criteria set or algorithm. Theinformation necessary for the functioning of similarity function 216 canbe maintained within search interaction data store 130 of system 100.

Historical influencing 218 can represent the algorithms andcorresponding values of significance associated with the actionsperformed by a current user and/or other users during previous, similarsearches. For instance, a user revisiting a Web page multiple timesafter an initial search, such as five visits within a week, can indicatethat the Web page was relevant to the user. The influence data forbrowsed Web pages generated by historical influencing 218 can bemaintained within search interaction data store 130 of system 100.

Legacy factors 220 can include factors, other than the browsing-timefactor, used to determine a Web page's relevancy for user provided querycriteria. GOOGLE.COM, for example, uses over two hundred relevancyfactors, which are each considered legacy factors 220 for purposes ofthe present invention.

Factor weighting engine 230 can represent the algorithms forsynthesizing legacy factors 220 and browsing-time factor components 210into a single value denoting page ranking. Different weights can beapplied to each of the different factors to make one factor moresignificant than others. For example, the factor weighting engine 230can assign a weight of twenty percent (20%) to a browsing-time factorand weights totally eighty percent (80%) to a combination of all otherrelevancy factors.

FIG. 3 is a collection 300 of illustrations depicting the influence of abrowsing-time factor on Web page search results in accordance with anembodiment of the inventive arrangements disclosed herein. Collection300 can be performed in the context of systems 100 and 200. The exampleof collection 300 is not limited in this regard, however, and can beperformed in the context of any system supporting the use of abrowsing-time factor for improving Web page search results.

Collection 300 contains query criteria 301, sample 305, and sample 330.Query criteria 301 contains text for purpose of searching Web pages tofind those Web pages that relevant to the text. For illustrativepurposes, query criteria 301 is defined as containing the text, “ShouldI wear my splint to bed?” and is used in both sample 305 and sample 330.Also, for the sake of simplicity, the illustrations of collection 300are set in a small-scale environment, such as a corporate network.

Sample 305 depicts the events that occur on Day 1. It should beappreciated that the designation “Day 1.” is merely for establishing achronological ordering of events and is not meant to express any actualcalendar date. Additionally, “Day 1.” designates an initial state of thesystem being depicting, such as system 100. This signifies that no dataexists in the system for the application of a browsing-time factor atthis time. Although it is possible for the other components of theresults refinement engine to have influence in an initial system state,those influences are not the focus of this collection of illustrations.

In sample 305, user 306 enters query criteria 301 into search provider310 via client 307 and network 308. Since this is the first query beingperformed, result refinement engine 312 does not have any data to rankthe Web pages listed in search results 315. Therefore, search results315 represents the set of Web pages determined by search provider 310 asapplicable to query criteria 301 and untouched by result refinementengine 312. For illustrative purposes, search results 315 contains theseWeb pages in the following order: www.splints4less.com/index.htm,www.medicalstudent.org/home.asp, and www.medicalinfo.com/splints.html.

Search results 315 is displayed to user 306 and browsing-time factordata 320 is collected. Browsing-time factor data 320 represents theamount of time spent by user 306 at the Web pages listed in searchresults 315. For illustrative purposes, browsing-time factor data 320shows that user 306 spent 30 seconds at www.splints4less.com/index.htm,12 minutes at www.medicalstudent.org/home.asp, and 7 hours atwww.medicalinfo.com/splints.html. This information can be collected bybehavior capture engine 113 or 114 and stored in behavior data store 116or 118 of system 100.

Sample 330 depicts the events that occur on Day 2. It should beappreciated that the designation “Day 2.” is meant to establish thatsample 330 occurs after sample 305 chronologically and is not meant toexpress any actual calendar date.

In sample 330, user 332 enters query criteria 301 into search provider310 via client 333 and network 308. Result refinement engine 312 canapply browsing-time factor data 320 of sample 305 to the set of Webpages determined applicable to query criteria 301 by search provider 310to rank the Web pages in a manner that is relevant to usage. Searchresults 340 represents the set of Web pages in order by the page rankingdetermined by result refinement engine 312. The Web pages contained insearch results 340 are in the order: www.medicalinfo.com/splints.html,www.medicalstudent.org/home.asp, and www.splints4less.com/index.htm.This order reflects browsing-time factor data 320 by ranking the Webpages by the amount of time spent at the page by user 306. Since user306 spent the most time at www.splints4less.com/index.htm, user 306found the information at this Web page the most helpful for querycriteria 301. Therefore, user 332 should be able to find their soughtafter information at this Web page as well.

Browsing-time factor data 345 represents the amount of time spent byuser 332 at the Web pages listed in search results 340. For illustrativepurposes, browsing-time factor data 345 shows that user 332 spent 0seconds at www.splints4less.com/index.htm, 0 seconds atwww.medicalstudent.org/home.asp, and 2 hours atwww.medicalinfo.com/splints.html. This information can be collected bybehavior capture engine 113 or 114 and stored in behavior data store 116or 118 of system 100. By using browsing-time factor data 320 toinfluence search results 340, user 332 is able to find the informationpertinent to their search quicker and more accurately than user 306.

It should be appreciated that the accuracy of the influence of abrowsing-time factor increases as the quantity of users and similarsearches increases. It should also be appreciated that the example ofFIG. 3 is an overly simplified example, which in addition to the statedbrowsing times could utilize any of the previously noted calculationevents when calculating the browsing-time factor. That is, time spentbrowsing a page is just one of many browsing-behavior based componentsthat can be used to calculate the browsing-time factor.

FIG. 4 is a flow chart of a method 400 for searching Web pages utilizinga browsing-time factor in accordance with an embodiment of the inventivearrangements disclosed herein. Method 400 can be performed in thecontext of system 100 and 200 or in the context of any other systemsupporting the use of a browsing-time factor for improving Web pagesearch results.

Method 400 can begin in step 405, where query criteria are received fromthe client. In step 410, the search provider retrieves a set of Webpages that correspond to the query criteria. Next, the search providerapplies the algorithms of the result refinement engine to the set of Webpages in step 415. Once the set of Web pages has been ordered by theresult refinement engine, step 420 occurs wherein the set of Web pagesare displayed within the search Web page.

In step 425, behavior capture engine optionally captures data thatcorresponds to how a user interacts with the ordered set of Web pages.Data can optionally be captured that measures the elapsed time before auser returns to the search provider in step 430. This data can becaptured by return-to-engine timer 122 of system 100 for use in futureapplications of the algorithms of result refinement engine 135.

FIG. 5 is a flow chart of a method 500 where a service agent canconfigure a system for searching Web pages utilizing a browsing-timefactor in accordance with an embodiment of the inventive arrangementsdisclosed herein. Method 500 can be preformed in the context of systems100, 200, and/or method 400.

Method 500 can begin in step 505, when a customer initiates a servicerequest. The service request can be a request for a service agent toestablish a new system for searching Web pages. The service request canalso be a request to troubleshoot a problem with an existing Web pagesearching system or to modify the relevancy factors of an existingresult refinement engine.

In step 510, a human agent can be selected to respond to the servicerequest. In step 515, the human agent can analyze a customer's currentsystem and can develop a solution. The solution can result in system 100or any system where Web pages can be searched with results improved by abrowsing-time factor, such as a system that performs the steps of method400.

In step 520, the human agent can configure the customer's system toinclude a search provider, a result refinement engine, and behaviorcapture engine. In step 525, the human agent can optionally add a resultrefinement engine and a behavior capture engine to a system that alreadycontains a search provider. The human agent can perform steps 520 and525 and/or can configure a computing device of the customer in a mannerthat the customer or clients of the customer can perform steps 520 and525 using the configured system in the future. For example, the serviceagent can load and configure software and hardware so that clientdevices will be capable of searching Web pages using a browsing-timefactor. In step 530, the human agent can complete the serviceactivities.

The present invention may be realized in hardware, software, or acombination of hardware and software. The present invention may berealized in a centralized fashion in one computer system, or in adistributed fashion where different elements are spread across severalinterconnected computer systems. Any kind of computer system or otherapparatus adapted for carrying out the methods described herein issuited. A typical combination of hardware and software may be a generalpurpose computer system with a computer program that, when being loadedand executed, controls the computer system such that it carries out themethods described herein.

The present invention also may be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

What is claimed is:
 1. A method for searching Web pages comprising:identifying query criteria entered into a search provider; determining aplurality of Web pages that satisfy the query criteria; ascertaining apage ranking for each of the plurality of Web pages, wherein each pageranking is based upon at least one relevancy factor; and presentingordered results for the query criteria, which are ordered by theascertained page rankings, wherein the at least one relevancy factorincludes a browsing-time factor, wherein the browsing-time factor isdetermined based upon a behavioral event of a user at the plurality ofWeb pages gathered by a behavioral capture engine, an elapsed time takenby the user before the user returns to the search provider gathered by areturn-to-engine timer, and a cumulative score calculated from a firstset of scores and a second set of scores, wherein the first set ofscores correspond to types of behavioral actions performed by the useron a browsed Web page gathered by the behavioral capture engine, whereinthe second set of scores correspond to time spent by the user at thebrowsed Web page and the elapsed time taken by the user to return to thesearch provider from the browsed Web page gathered by thereturn-to-engine timer, and wherein the browsing-time factor disregardsinput from a computing device in response to a fixed number of accesseswhich have occurred from the computing device per a predetermined timeperiod.
 2. The method of claim 1, further comprising: using client-sidesoftware to monitor time spent at the browsed Web page; and conveyingdata gathered by the client-side software to the search provider, saidsearch provider using the conveyed data to quantify the browsing-timefactor, wherein the browsing-time factor utilizes timing data gatheredby a return-to-engine timer and a behavior capture engine.
 3. The methodof claim 1, wherein the user enters the query criteria into a search Webpage, wherein presenting of results occurs via the search Web page, andwherein the browsing-time factor is quantified based upon a time ittakes for the user to return to the search Web page.
 4. The method ofclaim 1, wherein the time spent at the browsed Web page is determinedbased on previous browsing of the Web page and scrolling the Web page tosee a complete Web page, and wherein the time spent by the usercorresponds to an average time spent by a plurality of different usersat the browsed Web page responsive to a search based upon criteriasimilar to the query criteria.
 5. The method of claim 4, furthercomprising: limiting an affect that each of the different users has upondetermination of the time spent by the user, compared to the other onesof the different users so that no one user is able to dominate acalculation of the time spent at the browsed Web page by the user tocounteract excessive external influencing of search results.
 6. Themethod of claim 1, wherein the browsing-time factor is adjusted bydetecting a negative adjustment event which comprises at least one eventselected from a group consisting of not viewing, by the user, anentirety of the browsed Web page, accessing, by the user, other ones ofthe Web pages, and-entering, by the user, a similar criteria to thequery criteria a next time the search provider is used by the user. 7.The method of claim 1, wherein at least one of a service agent and acomputing device manipulated by the service agent perform theidentifying, the determining, the ascertaining, and the presenting inresponse to a service request.
 8. The method of claim 1, wherein thebrowsing-time factor is adjusted responsive to an adjustment event whichindicates a level of user interest in the browsed web page based onprevious browsing of the web page, and a time-out period of inactivitythat indicates abandonment of a computer.
 9. The method of claim 1,wherein the types of behavioral actions performed by the user is atleast one of downloading a file, printing a file, performing purchasetransaction, performing a large purchase transaction, and bookmarking apage.
 10. A method for ordering a set of Web pages satisfying querycriteria comprising: for each Web page in a set of Web pages satisfyingquery criteria, assigning a page ranking value based at least in partupon a browsing-time factor; ordering the Web pages in the set inaccordance with the assigned page ranking values; and presenting the Webpages in order within a search Web page in which the query criteria wasentered, wherein the browsing-time factor is determined based upon abehavioral event of a user at the plurality of Web pages gathered by abehavioral capture engine, an elapsed time taken by the user before theuser returns to the search provider gathered by a return-to-enginetimer, and a cumulative score calculated from a first set of scores anda second set of scores, wherein the first set of scores correspond totypes of behavioral actions performed by the user at an associatedbrowsed Web page gathered by the behavioral capture engine, and whereinthe second set of scores correspond to the elapsed time spent by the atthe associated browsed Web page and the elapsed time taken by the userto return to the search provider from the associated browsed Web pagegathered by the return-to-engine timer, wherein the browsing-time factordisregards input from a computing device in response to a fixed numberof accesses which have occurred from the computing device per apredetermined time period.
 11. The method of claim 10, wherein the timespent at the browsed Web page is determined based on previous browsingof the web page and scrolling the Web page to see a complete Web page,and wherein the time spent by the user corresponds to an average timespent by a plurality of different users at the browsed Web page inresponse to a search based upon criteria similar to the query criteria.12. The method of claim 11, further comprising: limiting an affect thateach of the different users has upon determination of the time spent bythe at least one user, compared to the other ones of the different usersso that no one user is able to dominate a calculation of the time spentat the browsed Web page by the at least one user to counteract excessiveexternal influencing of search results.
 13. The method of claim 10,wherein the browsing-time factor is adjusted by detecting a negativeadjustment event which comprises at least one event selected from agroup consisting of not viewing, by the user, an entirety of the browsedWeb page, accessing, by the user, other ones of the Web pages,and-entering, by the at least one user, similar criteria to the querycriteria a next time the search provider is used by the user.
 14. Themethod of claim 10, wherein the associated browsed Web page was browsedresponsive to the user selection of a hyperlink presented within thesearch web page, said hyperlink being presented as part of a result setfrom at least one previous query having criteria similar to the querycriteria.
 15. The method of claim 10, wherein the types of behavioralactions performed by the user is at least one of downloading a file,printing a file, performing purchase transaction, performing a largepurchase transaction, and bookmarking a page.
 16. A system for orderinga set of Web pages comprising: a computer executing a search providerconfigured to search Web pages for a user entered query criteria; and acomputer executing a result refinement engine configured to applyrelevancy algorithms, wherein said relevancy algorithms order Web pagesbased on at least one relevancy factor, wherein the at least onerelevancy factor includes a browsing-time factor, wherein thebrowsing-time factor is determined based upon a behavioral event of auser at the plurality of Web pages gathered by a behavioral captureengine, an elapsed time taken by the user before the user returns to thesearch provider gathered by a return-to-engine timer, and a cumulativescore calculated from a first set of scores and a second set of scores,wherein the first set of scores correspond to types of behavioralactions performed by the user on a browsed Web page gathered by thebehavioral capture engine, and wherein the second set of scorescorrespond to time spent by the user at the browsed Web page and theelapsed time taken by the user to return to the search provider from thebrowsed Web page gathered by a return-to-engine timer, wherein thebrowsing-time factor disregards input from a computing device inresponse to a fixed number of accesses which have occurred from thecomputing device per a predetermined time period.
 17. The system ofclaim 16, further comprising: at least one client, wherein the at leastone client includes a behavior capture engine configured to capture dataused to determine the browsing-time factor of the browsed Web page andstore data captured by the behavior capture engine in a behavior datastore.
 18. The system of claim 16, further comprising: a serverconfigured to serve a set of Web pages containing content derived fromresults of said search provider, wherein the server includes areturn-to-engine timer configured to capture data used to determine thebrowsing-time factor of the browsed Web page, wherein thereturn-to-engine timer comprises one of a server application, a localstand-alone application, a Web-based application, and an applet.
 19. Thesystem of claim 16, wherein the browsing time factor is adjusted bydetecting a positive adjustment event which comprises at least one eventselected from a group consisting of the user not accessing other Webpages included in the ordered results after the browsed Web page hasbeen accessed, the user entering dissimilar criteria from the querycriteria a next time the search provider is used by the user, and thetime for the user to return to a search Web page after having selectedthe browsed Web page from results presented within the search Web page.20. The system of claim 16, wherein the browsing time factor is adjustedresponsive to an adjustment event which indicates a level of userinterest in the browsed Web page based on previous browsing of the webpage, and a time-out period of inactivity that indicates abandonment ofa computer.